Additional Topics

Industries

Maria Ibanez

Maria Ibanez is a doctoral candidate and teaching fellow in the Technology and Operations Management unit at the Harvard Business School. She earned a Master of Science in Applied Economics from Marquette University. Prior to joining HBS, Maria conducted research at the University of Chicago with Professor Steven Levitt. Maria's research focuses on the interactions of management, organizational design, and productivity.

Maria Ibanez is a doctoral candidate and teaching fellow in the Technology and Operations Management unit at the Harvard Business School. She earned a Master of Science in Applied Economics from Marquette University. Prior to joining HBS, Maria conducted research at the University of Chicago with Professor Steven Levitt. Maria's research focuses on the interactions of management, organizational design, and productivity.

In her spare time, Maria enjoys volunteering, and has participated in medical brigades in Latin America as well as acted as a consultant for small businesses in need of technical expertise.

This paper is concerned with the market rental rate for space offered by commercial property and how that rental rate evolves over time. Rental rates reflect the value of the services provided by the property and can have a significant impact on the ability of its owners to make monthly debt obligations. We investigate commercial property rent dynamics for 34 large metropolitan areas in the U.S. The dynamics are studied from the second quarter of 1990 through the second quarter of 2009 and the results are compared across four property types or uses (office, industrial, flex, and retail). There is substantial heterogeneity in both the long and short run responses to changing demand and supply conditions. In general, the office market is the slowest to adjust back towards equilibrium while industrial and flex markets adjust back to the long run equilibrium very quickly. For industrial and office types, the speed of adjustment is substantially faster within quality segments and is strongest for grade A properties.

The question was how many units of inventory a manager should order when faced with a possible disruption in supply. The correct answer is not guesswork, but based on 150 years of theory and practice. We examine individual choices made in this critical situation—and the results are not encouraging.

A long line of research examines how best to schedule work to improve operational performance. This literature typically takes the perspective of a central planner who directs individuals to execute tasks in a prescribed order. In many settings, however, workers have discretion to deviate from the assigned order. This paper considers the operational implications of “discretionary task ordering,” defined as the task sequence resulting from an individual’s ability to select which task to complete next from a work queue. Using data from more than 2.4 million radiological studies read by 91 physicians over a period of two and a half years, we examine the conditions under which discretion is exercised to deviate from the assigned First-In-First-Out scheduling policy and the performance effects of those choices. Exploiting random assignment of tasks (cases) to doctors’ queues, together with variation in queue characteristics, we find that, on average, deviations lead to slower completion times, providing evidence of the costs of exercising discretion. Doctors tend to deviate more, and deviations tend to be less detrimental with experience, yet deviations remain harmful even for high levels of experience. Moreover, doctors tend to deviate to follow two common ordering strategies: shortest expected processing time and batching similar cases. Choosing the shortest tasks first is particularly detrimental for speed. Batching is associated with better performance when it occurs naturally, but not when it results from using discretion, suggesting that the benefit of repetition does not compensate for the cost of exercising discretion in this setting. Our research offers a behavioral perspective on queue management and highlights that discretion may have unintended negative costs.